All strong earthquakes are preceded by branching structures having different durations whose development scheme is partly largely predictable because it follows a well organized and recognizable pattern. By using a se...All strong earthquakes are preceded by branching structures having different durations whose development scheme is partly largely predictable because it follows a well organized and recognizable pattern. By using a seismic sequence hierarchization method, this study graphically explains the preparation process of an earthquake, called “branching structure”. In addition, criteria apt to distinguish the structures that will produce shocks of average magnitude from strong earthquakes’ will be defined. Based on the temporal oscillations of the magnitude values, we explain the procedure for identifying the developmental stages that characterize the energy accumulation stage of the branching structure, in order to early detect the energy release stage’s trigger point and obtain information on how it will develop over time. The study identifies also some pre-signals (trigger points) of various magnitudes in the energy release stage, which allows us to early predict the foreshocks and mainshock time position. The method we developed constitutes a truly innovative approach for the earthquake forecasting analysis, which dramatically differs from those developed so far, as it considers the structure of the seismic sequence not only as a magnitude values’ oscillation, but also as a sequence of developmental stages that may begin much earlier.展开更多
The performance of hematite(α-Fe_(2)O_(3))photoanodes for photoelectrochemical(PEC)water splitting has been limited to around 2-5 mA cm^(-2)under standard conditions due to their short hole diffusion length and slugg...The performance of hematite(α-Fe_(2)O_(3))photoanodes for photoelectrochemical(PEC)water splitting has been limited to around 2-5 mA cm^(-2)under standard conditions due to their short hole diffusion length and sluggish oxygen evolution reaction kinetics.This work overcomes those challenges through a synergistic strategy that co-designs the hematite architecture and the surface reaction pathway.We introduce a textured and hierarchically porous Ti-doped Fe_(2)O_(3)(tp-Fe_(2)O_(3))photoanode,synthesized via multi-cycle growth and flame annealing method.This unique architecture features a high texture(110),enlarged surface area,and hierarchically porous structure,which enable significantly enhanced bulk charge transport and interfacial charge transfer compared to typical nanorod Ti-doped Fe_(2)O_(3)(nr-Fe_(2)O_(3)).As a result,the tp-Fe_(2)O_(3)photoanode achieves a photocurrent density of 3.1 mA cm^(-2)at 1.23 V vs.RHE with exceptional stability over 105 h,notably without any co-catalyst.By replacing the OER with the hydrazine oxidation reaction,the photocurrent further reaches a record-high level of 7.1 mA cm^(-2)at 1.23 V_(RHE).Finally,when we integrate the tp-Fe_(2)O_(3)with a commercial Si solar cell,it achieves a solar-to-hydrogen efficiency of 8.7%-the highest reported value for any Fe_(2)O_(3)-based PVtandem system.This work provides critical insights into rational Fe_(2)O_(3)photoanode design and highlights the potential of hydrazine as an efficient alternative anodic reaction,enabling waste valorization.展开更多
Extreme cold weather seriously harms human thermoregulatory system,necessitating high-performance insulating garments to maintain body temperature.However,as the core insulating layer,advanced fibrous materials always...Extreme cold weather seriously harms human thermoregulatory system,necessitating high-performance insulating garments to maintain body temperature.However,as the core insulating layer,advanced fibrous materials always struggle to balance mechanical properties and thermal insulation,resulting in their inability to meet the demands for both washing resistance and personal protection.Herein,inspired by the natural spring-like structures of cucumber tendrils,a superelastic and washable micro/nanofibrous sponge(MNFS)based on biomimetic helical fibers is directly prepared utilizing multiple-jet electrospinning technology for high-performance thermal insulation.By regulating the conductivity of polyvinylidene fluoride solution,multiple-jet ejection and multiple-stage whipping of jets are achieved,and further control of phase separation rates enables the rapid solidification of jets to form spring-like helical fibers,which are directly entangled to assemble MNFS.The resulting MNFS exhibits superelasticity that can withstand large tensile strain(200%),1000 cyclic tensile or compression deformations,and retain good resilience even in liquid nitrogen(-196℃).Furthermore,the MNFS shows efficient thermal insulation with low thermal conductivity(24.85 mW m^(-1)K^(-1)),close to the value of dry air,and remains structural stability even after cyclic washing.This work offers new possibilities for advanced fibrous sponges in transportation,environmental,and energy applications.展开更多
Digital twin technology brings more opportunities and challenges to chemical engineering in both academic and industry.A complex process could have multiple digitalization needs,including simulation,monitoring,operato...Digital twin technology brings more opportunities and challenges to chemical engineering in both academic and industry.A complex process could have multiple digitalization needs,including simulation,monitoring,operator training,etc.;thus,a hierarchical digital twin would be a comprehensive solution to that.In this study,a novel and general framework of the digital twin is proposed for operations in process industry.With the hierarchical structure,the framework can handle various tasks driven by different roles in process industry,including managers,engineers,and operators.To complete these tasks,the framework consists of three modules:OAS(Operation Analysis System),OMS(Operation Monitoring System),and OTS(Operator Training System).Each module focuses on one unique type of demand from the staff,as well as interactions among them enabling efficient data sharing.Based on the hierarchical framework,a digital twin system is applied for one complex industrial nitration process,which successfully enhances the operation efficiency and safety in several industrial scenarios with different demands.展开更多
Herbicides are indispensable for safeguarding global crop production,yet their effectiveness is often undermined by extensive environmental losses during application.Using herbicide Diuron as a model compound,we devel...Herbicides are indispensable for safeguarding global crop production,yet their effectiveness is often undermined by extensive environmental losses during application.Using herbicide Diuron as a model compound,we developed hierarchical nanoparticles constructed through host-vip molecular recognition followed by electrostatic coassembly,yielding a formulation that unites high delivery efficiency with enhanced environmental compatibility.Relative to conventional wettable powders,these nanoparticles exhibited temperature-responsive release behavior and significantly enhanced foliar adhesion and deposition,increasing leaf retention by more than 241.7%.They also demonstrated strong resistance to rainfall wash-off and a markedly reduced propensity for groundwater leaching,with leaching losses decreased by approximately 18.6%.Greenhouse and field evaluations further confirmed their superior weed control under practical conditions,achieving control efficacies of up to 70.1%against Abutilon theophrasti and 52.9%against Setaria faberi,compared with 53.7%and 39.1%,respectively,for the commercial formulation at the same application rate.Extensive ecotoxicological assessments encompassing seed germination,zebrafish and earthworm assays,in vitro cellular tests,and in vivo rat studies consistently revealed an improved safety profile compared with commercial and technical formulations.Together,these results highlight hierarchical self-assembled nanoparticles as a promising platform for next-generation herbicide delivery that combines high target utilization with lower environmental impact and greater sustainability.展开更多
As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and ...As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and diverse communication needs.It is crucial to design control sequences with robust randomness and conflict-freeness to properly address differentiated access control in data link.In this paper,we propose a hierarchical access control scheme based on control sequences to achieve high utilization of time slots and differentiated access control.A theoretical bound of the hierarchical control sequence set is derived to characterize the constraints on the parameters of the sequence set.Moreover,two classes of optimal hierarchical control sequence sets satisfying the theoretical bound are constructed,both of which enable the scheme to achieve maximum utilization of time slots.Compared with the fixed time slot allocation scheme,our scheme reduces the symbol error rate by up to 9%,which indicates a significant improvement in anti-interference and eavesdropping capabilities.展开更多
Message structure reconstruction is a critical task in protocol reverse engineering,aiming to recover protocol field structures without access to source code.It enables important applications in network security,inclu...Message structure reconstruction is a critical task in protocol reverse engineering,aiming to recover protocol field structures without access to source code.It enables important applications in network security,including malware analysis and protocol fuzzing.However,existing methods suffer from inaccurate field boundary delineation and lack hierarchical relationship recovery,resulting in imprecise and incomplete reconstructions.In this paper,we propose ProRE,a novel method for reconstructing protocol field structures based on program execution slice embedding.ProRE extracts code slices from protocol parsing at runtime,converts them into embedding vectors using a data flow-sensitive assembly language model,and performs hierarchical clustering to recover complete protocol field structures.Evaluation on two datasets containing 12 protocols shows that ProRE achieves an average F1 score of 0.85 and a cophenetic correlation coefficient of 0.189,improving by 19%and 0.126%respectively over state-of-the-art methods(including BinPRE,Tupni,Netlifter,and QwQ-32B-preview),demonstrating significant superiority in both accuracy and completeness of field structure recovery.Case studies further validate the effectiveness of ProRE in practical malware analysis scenarios.展开更多
Modern business information systems face significant challenges in managing heterogeneous data sources,integrating disparate systems,and providing real-time decision support in complex enterprise environments.Contempo...Modern business information systems face significant challenges in managing heterogeneous data sources,integrating disparate systems,and providing real-time decision support in complex enterprise environments.Contemporary enterprises typically operate 200+interconnected systems,with research indicating that 52% of organizations manage three or more enterprise content management systems,creating information silos that reduce operational efficiency by up to 35%.While attention mechanisms have demonstrated remarkable success in natural language processing and computer vision,their systematic application to business information systems remains largely unexplored.This paper presents the theoretical foundation for a Hierarchical Attention-Based Business Information System(HABIS)framework that applies multi-level attention mechanisms to enterprise environments.We provide a comprehensive mathematical formulation of the framework,analyze its computational complexity,and present a proof-of-concept implementation with simulation-based validation that demonstrates a 42% reduction in crosssystem query latency compared to legacy ERP modules and 70% improvement in prediction accuracy over baseline methods.The theoretical framework introduces four hierarchical attention levels:system-level attention for dynamic weighting of business systems,process-level attention for business process prioritization,data-level attention for critical information selection,and temporal attention for time-sensitive pattern recognition.Our complexity analysis demonstrates that the framework achieves O(n log n)computational complexity for attention computation,making it scalable to large enterprise environments including retail supply chains with 200+system-scale deployments.The proof-of-concept implementation validates the theoretical framework’s feasibility withMSE loss of 0.439 and response times of 0.000120 s per query,demonstrating its potential for addressing key challenges in business information systems.This work establishes a foundation for future empirical research and practical implementation of attention-driven enterprise systems.展开更多
Accurate purchase prediction in e-commerce critically depends on the quality of behavioral features.This paper proposes a layered and interpretable feature engineering framework that organizes user signals into three ...Accurate purchase prediction in e-commerce critically depends on the quality of behavioral features.This paper proposes a layered and interpretable feature engineering framework that organizes user signals into three layers:Basic,Conversion&Stability(efficiency and volatility across actions),and Advanced Interactions&Activity(crossbehavior synergies and intensity).Using real Taobao(Alibaba’s primary e-commerce platform)logs(57,976 records for 10,203 users;25 November–03 December 2017),we conducted a hierarchical,layer-wise evaluation that holds data splits and hyperparameters fixed while varying only the feature set to quantify each layer’s marginal contribution.Across logistic regression(LR),decision tree,random forest,XGBoost,and CatBoost models with stratified 5-fold cross-validation,the performance improvedmonotonically fromBasic to Conversion&Stability to Advanced features.With LR,F1 increased from 0.613(Basic)to 0.962(Advanced);boosted models achieved high discrimination(0.995 AUC Score)and an F1 score up to 0.983.Calibration and precision–recall analyses indicated strong ranking quality and acknowledged potential dataset and period biases given the short(9-day)window.By making feature contributions measurable and reproducible,the framework complements model-centric advances and offers a transparent blueprint for production-grade behavioralmodeling.The code and processed artifacts are publicly available,and future work will extend the validation to longer,seasonal datasets and hybrid approaches that combine automated feature learning with domain-driven design.展开更多
To shield electronics from complicated electromagnetic environments caused by wireless electromagnetic waves,achieving elaborately structural manufacturing while not sacrificing electromagnetic interference shielding ...To shield electronics from complicated electromagnetic environments caused by wireless electromagnetic waves,achieving elaborately structural manufacturing while not sacrificing electromagnetic interference shielding performances remains crucial challenges.Herein,we propose a hierarchical manufacturing method that combines the use of 3D printing shear flow field and layer-by-layer assembly for fabricating the structurally customizable and multifunctional polylactic acid@graphene nanoparticle(PLA@GNs)materials.The dynamic behavior of polymer fluids is firstly explored via computational fluid dynamic simulation,and a Weissenberg number is employed to quantitatively analyze the disordered-to-ordered structural evolution of molecular chains and nanoparticles,allowing to tailor the micro-scale ordered structures.Subsequently,the macro-scale 3D architectures of PLA@GNs modules are fabricated by layer-by-layer assembly.Owing to the aligned GNs,the shielding performance reaches 41.2 d B,simultaneously accompanied by a directional thermal conductivity of 3.2 W m^(-1)K^(-1).Moreover,the potential application of 3D-printed shielding modules in specific civilian frequency bands such as 4G(1800–2100 MHz),Bluetooth(2402–2480 MHz),and 5G(3300–3800 MHz)is fully demonstrated.Overall,this work not only establishes a universal methodology about 3D printing shear flow field-driven orientation of two-dimensional nanoparticles within polymer fluids,but also gives a scientific method for advanced manufacturing of the next-generation electromagnetic functional modules for smart electronics.展开更多
Multivariate time series forecasting plays a crucial role in decision-making for systems like energy grids and transportation networks,where temporal patterns emerge across diverse scales from short-term fluctuations ...Multivariate time series forecasting plays a crucial role in decision-making for systems like energy grids and transportation networks,where temporal patterns emerge across diverse scales from short-term fluctuations to long-term trends.However,existing Transformer-based methods often process data at a single resolution or handle multiple scales independently,overlooking critical cross-scale interactions that influence prediction accuracy.To address this gap,we introduce the Hierarchical Attention Transformer(HAT),which enables direct information exchange between temporal hierarchies through a novel cross-scale attention mechanism.HAT extracts multi-scale features using hierarchical convolutional-recurrent blocks,fuses them via temperature-controlled mechanisms,and optimizes gradient flow with residual connections for stable training.Evaluations on eight benchmark datasets show HAT outperforming state-of-the-art baselines,with average reductions of 8.2%in MSE and 7.5%in MAE across horizons,while achieving a 6.1×training speedup over patch-based methods.These advancements highlight HAT’s potential for applications requiring multi-resolution temporal modeling.展开更多
In the context of modern software development characterized by increasing complexity and compressed development cycles,traditional static vulnerability detection methods face prominent challenges including high false ...In the context of modern software development characterized by increasing complexity and compressed development cycles,traditional static vulnerability detection methods face prominent challenges including high false positive rates and missed detections of complex logic due to their over-reliance on rule templates.This paper proposes a Syntax-Aware Hierarchical Attention Network(SAHAN)model,which achieves high-precision vulnerability detection through grammar-rule-driven multi-granularity code slicing and hierarchical semantic fusion mechanisms.The SAHAN model first generates Syntax Independent Units(SIUs),which slices the code based on Abstract Syntax Tree(AST)and predefined grammar rules,retaining vulnerability-sensitive contexts.Following this,through a hierarchical attention mechanism,the local syntax-aware layer encodes fine-grained patterns within SIUs,while the global semantic correlation layer captures vulnerability chains across SIUs,achieving synergistic modeling of syntax and semantics.Experiments show that on benchmark datasets like QEMU,SAHAN significantly improves detection performance by 4.8%to 13.1%on average compared to baseline models such as Devign and VulDeePecker.展开更多
Soft grippers research is gaining increasing attention for their flexibility.However,the conventional soft gripper primar-ily focuses on soft fingers,without considering the palm.This makes grasping forces concentrate...Soft grippers research is gaining increasing attention for their flexibility.However,the conventional soft gripper primar-ily focuses on soft fingers,without considering the palm.This makes grasping forces concentrated in the fingertip areas,resulting in objects being prone to damage and instability during handling,especially for delicate items.Additionally,pre-transportation classification faces challenges:tactile methods are complex,visual methods are environment-sensitive,and both struggle with similar objects.To address these problems,inspired by the human hand's transition between finger grasp and palm support and the lotus's hierarchical structure,this paper proposes a dual-layer gripper,named IOSGrip-per.It features four pneumatic soft fingers and a rotational soft-rigid palm.Through coordinated control of the fingers and palm,it transitions concentrated fingertip squeeze force to distributed palm support force,reducing squeeze force and squeeze duration.Moreover,it integrates a range sensor and four load cells,enabling stable and accurate measurements of the object's height and weight.Furthermore,a classifier is developed based on K-nearest neighbor algorithm,allowing real-time object classification.Experiments demonstrate that compared to only using soft fingers,the IOSGripper signifi-cantly reduces the squeeze force on the objects(with 0 N squeeze force during palm support)and damage on the delicate object,while improving grasping stability.Its height and weight measurement errors are within 2 mm and 1 g,respectively.And it achieves high accuracy in three test scenarios,including classifying similar objects.This study provides useful insights for the design of soft grippers capable of human-like grasping and sorting tasks.展开更多
With the growing integration of renewable energy sources(RESs)and smart interconnected devices,conventional distribution networks have turned to active distribution networks(ADNs)with complex system model and power fl...With the growing integration of renewable energy sources(RESs)and smart interconnected devices,conventional distribution networks have turned to active distribution networks(ADNs)with complex system model and power flow dynamics.The rapid fluctuation of RES power may easily result in frequent voltage violation issues.Taking the flexible RES reactive power as control variables,this paper proposes a two-layer control scheme with Koopman wide neural network(WNN)based model predictive control(MPC)method for optimal voltage regulation and network loss reduction.Based on Koopman operator theory,a data-driven WNN method is presented to fit a high-dimensional linear model of power flow.With the model,voltage and network loss sensitivities are computed analytically,and utilized for ADN partition and control model formulation.In the lower level,a dual-mode adaptive switching MPC strategy is put forward for optimal voltage control and network loss optimization in each individual partition to decide the RES reactive power.The upper level is to calculate the adjustment coefficients of the RES reactive power given in the low level by taking the coupling effects of different partitions into account,and then the final reactive power dispatches of RESs are obtained to realize optimal control of voltage and network loss.Simulation results on two ADNs demonstrate that the proposed strategy can reliably maintain the voltage at each node within the secure range,reduce network power losses,and enhance the overall system security and economic efficiency.展开更多
Understory plants are an integral part of forests,serving a variety of functions that help maintain healthy ecosystems.The structure and composition of the understory are influenced by numerous biotic and abiotic fact...Understory plants are an integral part of forests,serving a variety of functions that help maintain healthy ecosystems.The structure and composition of the understory are influenced by numerous biotic and abiotic factors,with light being critical.The introduction of the pathogen Cronartium ribicola,which causes white pine blister rust,into North America in the early 20 th century led to the near total loss of western white pine(Pinus monticola)from moist forests of the Northern Rockies.Management is reintroducing blister rust-resistant western white pine across the landscape,but the effects on the understory are unknown.We examined the effects of stand structure and proportion of western white pine in the overstory on understory diversity of vascular plants in closed canopy stands dominated by blister rust-resistant western white pine across northern Idaho.Habitat series explained the greatest amount of variation(34%)in species presence-absence,while canopy cover accounted for 25%,basal area of all trees for 18%,and the proportion of western white pine composition by 14%.Our analysis revealed positive relationships between the proportion of western white pine in the overstory and both the presence of understory plants and the cover of several understory species.For both the presence and cover,separate sets of thirteen species were found to have a positive relationship with the proportion of western white pine in the overstory,with eight species in common.This research fills a knowledge gap by using data from a range of stands across northern Idaho with varying abundance of western white pine in the overstory to evaluate the relationship between the understory and overstory composition.As land managers plant more western white pine trees,we are likely to see the concomitant increase in understory plant diversity across the landscape,in addition to numerous other benefits,including disturbance resistance and resilience.展开更多
As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,t...As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,the heterogeneity of aircraft,partial observability,and dynamic uncertainty in operational airspace pose significant challenges to autonomous collision avoidance using traditional methods.To address these issues,this paper proposes an adaptive collision avoidance approach for UAVs based on deep reinforcement learning.First,a unified uncertainty model incorporating dynamic wind fields is constructed to capture the complexity of joint operational environments.Then,to effectively handle the heterogeneity between manned and unmanned aircraft and the limitations of dynamic observations,a sector-based partial observation mechanism is designed.A Dynamic Threat Prioritization Assessment algorithm is also proposed to evaluate potential collision threats from multiple dimensions,including time to closest approach,minimum separation distance,and aircraft type.Furthermore,a Hierarchical Prioritized Experience Replay(HPER)mechanism is introduced,which classifies experience samples into high,medium,and low priority levels to preferentially sample critical experiences,thereby improving learning efficiency and accelerating policy convergence.Simulation results show that the proposed HPER-D3QN algorithm outperforms existing methods in terms of learning speed,environmental adaptability,and robustness,significantly enhancing collision avoidance performance and convergence rate.Finally,transfer experiments on a high-fidelity battlefield airspace simulation platform validate the proposed method's deployment potential and practical applicability in complex,real-world joint operational scenarios.展开更多
AIM:To evaluate long-term visual field(VF)prediction using K-means clustering in patients with primary open angle glaucoma(POAG).METHODS:Patients who underwent 24-2 VF tests≥10 were included in this study.Using 52 to...AIM:To evaluate long-term visual field(VF)prediction using K-means clustering in patients with primary open angle glaucoma(POAG).METHODS:Patients who underwent 24-2 VF tests≥10 were included in this study.Using 52 total deviation values(TDVs)from the first 10 VF tests of the training dataset,VF points were clustered into several regions using the hierarchical ordered partitioning and collapsing hybrid(HOPACH)and K-means clustering.Based on the clustering results,a linear regression analysis was applied to each clustered region of the testing dataset to predict the TDVs of the 10th VF test.Three to nine VF tests were used to predict the 10th VF test,and the prediction errors(root mean square error,RMSE)of each clustering method and pointwise linear regression(PLR)were compared.RESULTS:The training group consisted of 228 patients(mean age,54.20±14.38y;123 males and 105 females),and the testing group included 81 patients(mean age,54.88±15.22y;43 males and 38 females).All subjects were diagnosed with POAG.Fifty-two VF points were clustered into 11 and nine regions using HOPACH and K-means clustering,respectively.K-means clustering had a lower prediction error than PLR when n=1:3 and 1:4(both P≤0.003).The prediction errors of K-means clustering were lower than those of HOPACH in all sections(n=1:4 to 1:9;all P≤0.011),except for n=1:3(P=0.680).PLR outperformed K-means clustering only when n=1:8 and 1:9(both P≤0.020).CONCLUSION:K-means clustering can predict longterm VF test results more accurately in patients with POAG with limited VF data.展开更多
For the ultra HVDC(UHVDC)with the hierarchical connection mode at the inverter side,considering the change of the Thevenin equivalent parameters(TEP)of post-fault AC grid,a coordinated control strategy to the subseque...For the ultra HVDC(UHVDC)with the hierarchical connection mode at the inverter side,considering the change of the Thevenin equivalent parameters(TEP)of post-fault AC grid,a coordinated control strategy to the subsequent commutation failure(SCF)at both layers is newly proposed.The originality of this work is manifested in three aspects.1)The mechanism of the SCF at the fault layer is newly found by deriving the analytical expression of the extinction angle with the TEP,and that at the non-fault layer is newly found by the voltage-time area theory with the DC current coupling.2)An estimation model for the TEPs of two AC grids at the inverter side is proposed with the post-fault quantities.To address the random noise and inaccurate measurement data,an adaptive robust least squares method based on the median principle is proposed to solve the TEP model.3)A coordinated control strategy with the estimated TEP is proposed to compensate for the extinction angle at the fault layer and limit the DC current at the non-fault layer,thus suppressing the SCF.The simulation results verify the suppression effect of the proposed control on the SCF under different fault conditions.展开更多
In this work,the Hierarchical Quadrature Element Method(HQEM)formulation of geometrically exact shells is proposed and applied for geometrically nonlinear analyses of both isotropic and laminated shells.The stress res...In this work,the Hierarchical Quadrature Element Method(HQEM)formulation of geometrically exact shells is proposed and applied for geometrically nonlinear analyses of both isotropic and laminated shells.The stress resultant formulation is developed within the HQEM framework,consequently significantly simplifying the computations of residual force and stiffness matrix.The present formulation inherently avoids shear and membrane locking,benefiting from its high-order approximation property.Furthermore,HQEM’s independent nodal distribution capability conveniently supports local p-refinement and flexibly facilitates mesh generation in various structural configurations through the combination of quadrilateral and triangular elements.Remarkably,in lateral buckling analysis,the HQEM outperforms the weak-form quadrilateral element(QEM)in accuracy with identical nodal degrees of freedom(three displacements and two rotations).Under high-load nonlinear response,the QEM exhibits a maximum relative deviation of approximately 9.5%from the reference,while the HQEM remains closely aligned with the benchmark results.In addition,for the cantilever beam under tip moment,HQEM produces virtually no out-of-plane deviation,compared to a slight deviation of 0.00001 with QEM,confirming its superior numerical reliability.In summary,the method demonstrates high accuracy,superior convergence,and robustness in handling large rotations and complex post-buckling behaviors across a series of benchmark problems.展开更多
By 2025,research on Traditional Chinese Medicine(TCM)meridians has generated 12-15 macro-level theories and over 20 specific hypotheses,manifesting a highly fragmented research landscape.Objective:This paper proposes ...By 2025,research on Traditional Chinese Medicine(TCM)meridians has generated 12-15 macro-level theories and over 20 specific hypotheses,manifesting a highly fragmented research landscape.Objective:This paper proposes the“Holistic Hierarchical Predictive-Integration Hypothesis”(HHPIT)to construct a unified theoretical framework that integrates the rational components of existing meridian hypotheses.Methods:The HHPIT hypothesis systematically reviews current meridian theories,employs interdisciplinary methodologies,integrates artificial intelligence technology,and establishes a three-tier architecture encompassing structural,functional,and systemic layers.Results:HHPIT successfully integrates diverse meridian theories,proposes a computable algorithmic pipeline,and provides specific application protocols for chronic disease treatment,anti-aging,and enhancement of Zang-fu organ functions.Conclusion:HHPIT offers a novel,computable,and verifiable research paradigm for meridian studies,promoting the modernization and internationalization of TCM theory.展开更多
文摘All strong earthquakes are preceded by branching structures having different durations whose development scheme is partly largely predictable because it follows a well organized and recognizable pattern. By using a seismic sequence hierarchization method, this study graphically explains the preparation process of an earthquake, called “branching structure”. In addition, criteria apt to distinguish the structures that will produce shocks of average magnitude from strong earthquakes’ will be defined. Based on the temporal oscillations of the magnitude values, we explain the procedure for identifying the developmental stages that characterize the energy accumulation stage of the branching structure, in order to early detect the energy release stage’s trigger point and obtain information on how it will develop over time. The study identifies also some pre-signals (trigger points) of various magnitudes in the energy release stage, which allows us to early predict the foreshocks and mainshock time position. The method we developed constitutes a truly innovative approach for the earthquake forecasting analysis, which dramatically differs from those developed so far, as it considers the structure of the seismic sequence not only as a magnitude values’ oscillation, but also as a sequence of developmental stages that may begin much earlier.
基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.RS-2024-00335976)。
文摘The performance of hematite(α-Fe_(2)O_(3))photoanodes for photoelectrochemical(PEC)water splitting has been limited to around 2-5 mA cm^(-2)under standard conditions due to their short hole diffusion length and sluggish oxygen evolution reaction kinetics.This work overcomes those challenges through a synergistic strategy that co-designs the hematite architecture and the surface reaction pathway.We introduce a textured and hierarchically porous Ti-doped Fe_(2)O_(3)(tp-Fe_(2)O_(3))photoanode,synthesized via multi-cycle growth and flame annealing method.This unique architecture features a high texture(110),enlarged surface area,and hierarchically porous structure,which enable significantly enhanced bulk charge transport and interfacial charge transfer compared to typical nanorod Ti-doped Fe_(2)O_(3)(nr-Fe_(2)O_(3)).As a result,the tp-Fe_(2)O_(3)photoanode achieves a photocurrent density of 3.1 mA cm^(-2)at 1.23 V vs.RHE with exceptional stability over 105 h,notably without any co-catalyst.By replacing the OER with the hydrazine oxidation reaction,the photocurrent further reaches a record-high level of 7.1 mA cm^(-2)at 1.23 V_(RHE).Finally,when we integrate the tp-Fe_(2)O_(3)with a commercial Si solar cell,it achieves a solar-to-hydrogen efficiency of 8.7%-the highest reported value for any Fe_(2)O_(3)-based PVtandem system.This work provides critical insights into rational Fe_(2)O_(3)photoanode design and highlights the potential of hydrazine as an efficient alternative anodic reaction,enabling waste valorization.
基金supported by Young Elite Scientists Sponsorship Program by China Association for Science and Technology(No.2022QNRC001)the National Natural Science Foundation of China(No.52273053)the Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission(No.21CGA41)。
文摘Extreme cold weather seriously harms human thermoregulatory system,necessitating high-performance insulating garments to maintain body temperature.However,as the core insulating layer,advanced fibrous materials always struggle to balance mechanical properties and thermal insulation,resulting in their inability to meet the demands for both washing resistance and personal protection.Herein,inspired by the natural spring-like structures of cucumber tendrils,a superelastic and washable micro/nanofibrous sponge(MNFS)based on biomimetic helical fibers is directly prepared utilizing multiple-jet electrospinning technology for high-performance thermal insulation.By regulating the conductivity of polyvinylidene fluoride solution,multiple-jet ejection and multiple-stage whipping of jets are achieved,and further control of phase separation rates enables the rapid solidification of jets to form spring-like helical fibers,which are directly entangled to assemble MNFS.The resulting MNFS exhibits superelasticity that can withstand large tensile strain(200%),1000 cyclic tensile or compression deformations,and retain good resilience even in liquid nitrogen(-196℃).Furthermore,the MNFS shows efficient thermal insulation with low thermal conductivity(24.85 mW m^(-1)K^(-1)),close to the value of dry air,and remains structural stability even after cyclic washing.This work offers new possibilities for advanced fibrous sponges in transportation,environmental,and energy applications.
基金support of the“Pioneer”and“Leading Goose”Research&Development Program of Zhejiang(2024C01028)the State Key Laboratory of Industrial Control Technology,China(ICT2024C04)are gratefully acknowledged.
文摘Digital twin technology brings more opportunities and challenges to chemical engineering in both academic and industry.A complex process could have multiple digitalization needs,including simulation,monitoring,operator training,etc.;thus,a hierarchical digital twin would be a comprehensive solution to that.In this study,a novel and general framework of the digital twin is proposed for operations in process industry.With the hierarchical structure,the framework can handle various tasks driven by different roles in process industry,including managers,engineers,and operators.To complete these tasks,the framework consists of three modules:OAS(Operation Analysis System),OMS(Operation Monitoring System),and OTS(Operator Training System).Each module focuses on one unique type of demand from the staff,as well as interactions among them enabling efficient data sharing.Based on the hierarchical framework,a digital twin system is applied for one complex industrial nitration process,which successfully enhances the operation efficiency and safety in several industrial scenarios with different demands.
基金supported by the University Synergy Innovation Program of Anhui Province(GXXT-2021-059)the National Key Research and Development Program of China(2023YFD1702102)the Major Natural Science Research Project of Anhui Universities(2023AH040143).
文摘Herbicides are indispensable for safeguarding global crop production,yet their effectiveness is often undermined by extensive environmental losses during application.Using herbicide Diuron as a model compound,we developed hierarchical nanoparticles constructed through host-vip molecular recognition followed by electrostatic coassembly,yielding a formulation that unites high delivery efficiency with enhanced environmental compatibility.Relative to conventional wettable powders,these nanoparticles exhibited temperature-responsive release behavior and significantly enhanced foliar adhesion and deposition,increasing leaf retention by more than 241.7%.They also demonstrated strong resistance to rainfall wash-off and a markedly reduced propensity for groundwater leaching,with leaching losses decreased by approximately 18.6%.Greenhouse and field evaluations further confirmed their superior weed control under practical conditions,achieving control efficacies of up to 70.1%against Abutilon theophrasti and 52.9%against Setaria faberi,compared with 53.7%and 39.1%,respectively,for the commercial formulation at the same application rate.Extensive ecotoxicological assessments encompassing seed germination,zebrafish and earthworm assays,in vitro cellular tests,and in vivo rat studies consistently revealed an improved safety profile compared with commercial and technical formulations.Together,these results highlight hierarchical self-assembled nanoparticles as a promising platform for next-generation herbicide delivery that combines high target utilization with lower environmental impact and greater sustainability.
基金supported by the National Science Foundation of China(No.62171387)the Science and Technology Program of Sichuan Province(No.2024NSFSC0468)the China Postdoctoral Science Foundation(No.2019M663475).
文摘As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and diverse communication needs.It is crucial to design control sequences with robust randomness and conflict-freeness to properly address differentiated access control in data link.In this paper,we propose a hierarchical access control scheme based on control sequences to achieve high utilization of time slots and differentiated access control.A theoretical bound of the hierarchical control sequence set is derived to characterize the constraints on the parameters of the sequence set.Moreover,two classes of optimal hierarchical control sequence sets satisfying the theoretical bound are constructed,both of which enable the scheme to achieve maximum utilization of time slots.Compared with the fixed time slot allocation scheme,our scheme reduces the symbol error rate by up to 9%,which indicates a significant improvement in anti-interference and eavesdropping capabilities.
文摘Message structure reconstruction is a critical task in protocol reverse engineering,aiming to recover protocol field structures without access to source code.It enables important applications in network security,including malware analysis and protocol fuzzing.However,existing methods suffer from inaccurate field boundary delineation and lack hierarchical relationship recovery,resulting in imprecise and incomplete reconstructions.In this paper,we propose ProRE,a novel method for reconstructing protocol field structures based on program execution slice embedding.ProRE extracts code slices from protocol parsing at runtime,converts them into embedding vectors using a data flow-sensitive assembly language model,and performs hierarchical clustering to recover complete protocol field structures.Evaluation on two datasets containing 12 protocols shows that ProRE achieves an average F1 score of 0.85 and a cophenetic correlation coefficient of 0.189,improving by 19%and 0.126%respectively over state-of-the-art methods(including BinPRE,Tupni,Netlifter,and QwQ-32B-preview),demonstrating significant superiority in both accuracy and completeness of field structure recovery.Case studies further validate the effectiveness of ProRE in practical malware analysis scenarios.
文摘Modern business information systems face significant challenges in managing heterogeneous data sources,integrating disparate systems,and providing real-time decision support in complex enterprise environments.Contemporary enterprises typically operate 200+interconnected systems,with research indicating that 52% of organizations manage three or more enterprise content management systems,creating information silos that reduce operational efficiency by up to 35%.While attention mechanisms have demonstrated remarkable success in natural language processing and computer vision,their systematic application to business information systems remains largely unexplored.This paper presents the theoretical foundation for a Hierarchical Attention-Based Business Information System(HABIS)framework that applies multi-level attention mechanisms to enterprise environments.We provide a comprehensive mathematical formulation of the framework,analyze its computational complexity,and present a proof-of-concept implementation with simulation-based validation that demonstrates a 42% reduction in crosssystem query latency compared to legacy ERP modules and 70% improvement in prediction accuracy over baseline methods.The theoretical framework introduces four hierarchical attention levels:system-level attention for dynamic weighting of business systems,process-level attention for business process prioritization,data-level attention for critical information selection,and temporal attention for time-sensitive pattern recognition.Our complexity analysis demonstrates that the framework achieves O(n log n)computational complexity for attention computation,making it scalable to large enterprise environments including retail supply chains with 200+system-scale deployments.The proof-of-concept implementation validates the theoretical framework’s feasibility withMSE loss of 0.439 and response times of 0.000120 s per query,demonstrating its potential for addressing key challenges in business information systems.This work establishes a foundation for future empirical research and practical implementation of attention-driven enterprise systems.
基金supported by the research fund of Hanyang University(HY-202500000001616).
文摘Accurate purchase prediction in e-commerce critically depends on the quality of behavioral features.This paper proposes a layered and interpretable feature engineering framework that organizes user signals into three layers:Basic,Conversion&Stability(efficiency and volatility across actions),and Advanced Interactions&Activity(crossbehavior synergies and intensity).Using real Taobao(Alibaba’s primary e-commerce platform)logs(57,976 records for 10,203 users;25 November–03 December 2017),we conducted a hierarchical,layer-wise evaluation that holds data splits and hyperparameters fixed while varying only the feature set to quantify each layer’s marginal contribution.Across logistic regression(LR),decision tree,random forest,XGBoost,and CatBoost models with stratified 5-fold cross-validation,the performance improvedmonotonically fromBasic to Conversion&Stability to Advanced features.With LR,F1 increased from 0.613(Basic)to 0.962(Advanced);boosted models achieved high discrimination(0.995 AUC Score)and an F1 score up to 0.983.Calibration and precision–recall analyses indicated strong ranking quality and acknowledged potential dataset and period biases given the short(9-day)window.By making feature contributions measurable and reproducible,the framework complements model-centric advances and offers a transparent blueprint for production-grade behavioralmodeling.The code and processed artifacts are publicly available,and future work will extend the validation to longer,seasonal datasets and hybrid approaches that combine automated feature learning with domain-driven design.
基金financially supported by the National Natural Science Foundation of China(52303036)the Natural Science Foundation of Guangxi(2024GXNSFBA010123)+2 种基金the International Science&Technology Innovation Cooperation Project of Sichuan Province(2024YFHZ0232)the International Science&Technology Cooperation Project of Chengdu(2021-GH03-00009-HZ)the Opening Project of State Key Laboratory of Polymer Materials Engineering(Sichuan University)(Sklpme2023-3-18)。
文摘To shield electronics from complicated electromagnetic environments caused by wireless electromagnetic waves,achieving elaborately structural manufacturing while not sacrificing electromagnetic interference shielding performances remains crucial challenges.Herein,we propose a hierarchical manufacturing method that combines the use of 3D printing shear flow field and layer-by-layer assembly for fabricating the structurally customizable and multifunctional polylactic acid@graphene nanoparticle(PLA@GNs)materials.The dynamic behavior of polymer fluids is firstly explored via computational fluid dynamic simulation,and a Weissenberg number is employed to quantitatively analyze the disordered-to-ordered structural evolution of molecular chains and nanoparticles,allowing to tailor the micro-scale ordered structures.Subsequently,the macro-scale 3D architectures of PLA@GNs modules are fabricated by layer-by-layer assembly.Owing to the aligned GNs,the shielding performance reaches 41.2 d B,simultaneously accompanied by a directional thermal conductivity of 3.2 W m^(-1)K^(-1).Moreover,the potential application of 3D-printed shielding modules in specific civilian frequency bands such as 4G(1800–2100 MHz),Bluetooth(2402–2480 MHz),and 5G(3300–3800 MHz)is fully demonstrated.Overall,this work not only establishes a universal methodology about 3D printing shear flow field-driven orientation of two-dimensional nanoparticles within polymer fluids,but also gives a scientific method for advanced manufacturing of the next-generation electromagnetic functional modules for smart electronics.
文摘Multivariate time series forecasting plays a crucial role in decision-making for systems like energy grids and transportation networks,where temporal patterns emerge across diverse scales from short-term fluctuations to long-term trends.However,existing Transformer-based methods often process data at a single resolution or handle multiple scales independently,overlooking critical cross-scale interactions that influence prediction accuracy.To address this gap,we introduce the Hierarchical Attention Transformer(HAT),which enables direct information exchange between temporal hierarchies through a novel cross-scale attention mechanism.HAT extracts multi-scale features using hierarchical convolutional-recurrent blocks,fuses them via temperature-controlled mechanisms,and optimizes gradient flow with residual connections for stable training.Evaluations on eight benchmark datasets show HAT outperforming state-of-the-art baselines,with average reductions of 8.2%in MSE and 7.5%in MAE across horizons,while achieving a 6.1×training speedup over patch-based methods.These advancements highlight HAT’s potential for applications requiring multi-resolution temporal modeling.
基金supported by the research start-up funds for invited doctor of Lanzhou University of Technology under Grant 14/062402。
文摘In the context of modern software development characterized by increasing complexity and compressed development cycles,traditional static vulnerability detection methods face prominent challenges including high false positive rates and missed detections of complex logic due to their over-reliance on rule templates.This paper proposes a Syntax-Aware Hierarchical Attention Network(SAHAN)model,which achieves high-precision vulnerability detection through grammar-rule-driven multi-granularity code slicing and hierarchical semantic fusion mechanisms.The SAHAN model first generates Syntax Independent Units(SIUs),which slices the code based on Abstract Syntax Tree(AST)and predefined grammar rules,retaining vulnerability-sensitive contexts.Following this,through a hierarchical attention mechanism,the local syntax-aware layer encodes fine-grained patterns within SIUs,while the global semantic correlation layer captures vulnerability chains across SIUs,achieving synergistic modeling of syntax and semantics.Experiments show that on benchmark datasets like QEMU,SAHAN significantly improves detection performance by 4.8%to 13.1%on average compared to baseline models such as Devign and VulDeePecker.
基金the Major research program of national natural science foundation of China(91848206).
文摘Soft grippers research is gaining increasing attention for their flexibility.However,the conventional soft gripper primar-ily focuses on soft fingers,without considering the palm.This makes grasping forces concentrated in the fingertip areas,resulting in objects being prone to damage and instability during handling,especially for delicate items.Additionally,pre-transportation classification faces challenges:tactile methods are complex,visual methods are environment-sensitive,and both struggle with similar objects.To address these problems,inspired by the human hand's transition between finger grasp and palm support and the lotus's hierarchical structure,this paper proposes a dual-layer gripper,named IOSGrip-per.It features four pneumatic soft fingers and a rotational soft-rigid palm.Through coordinated control of the fingers and palm,it transitions concentrated fingertip squeeze force to distributed palm support force,reducing squeeze force and squeeze duration.Moreover,it integrates a range sensor and four load cells,enabling stable and accurate measurements of the object's height and weight.Furthermore,a classifier is developed based on K-nearest neighbor algorithm,allowing real-time object classification.Experiments demonstrate that compared to only using soft fingers,the IOSGripper signifi-cantly reduces the squeeze force on the objects(with 0 N squeeze force during palm support)and damage on the delicate object,while improving grasping stability.Its height and weight measurement errors are within 2 mm and 1 g,respectively.And it achieves high accuracy in three test scenarios,including classifying similar objects.This study provides useful insights for the design of soft grippers capable of human-like grasping and sorting tasks.
基金supported by the Science and Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.(J2024162).
文摘With the growing integration of renewable energy sources(RESs)and smart interconnected devices,conventional distribution networks have turned to active distribution networks(ADNs)with complex system model and power flow dynamics.The rapid fluctuation of RES power may easily result in frequent voltage violation issues.Taking the flexible RES reactive power as control variables,this paper proposes a two-layer control scheme with Koopman wide neural network(WNN)based model predictive control(MPC)method for optimal voltage regulation and network loss reduction.Based on Koopman operator theory,a data-driven WNN method is presented to fit a high-dimensional linear model of power flow.With the model,voltage and network loss sensitivities are computed analytically,and utilized for ADN partition and control model formulation.In the lower level,a dual-mode adaptive switching MPC strategy is put forward for optimal voltage control and network loss optimization in each individual partition to decide the RES reactive power.The upper level is to calculate the adjustment coefficients of the RES reactive power given in the low level by taking the coupling effects of different partitions into account,and then the final reactive power dispatches of RESs are obtained to realize optimal control of voltage and network loss.Simulation results on two ADNs demonstrate that the proposed strategy can reliably maintain the voltage at each node within the secure range,reduce network power losses,and enhance the overall system security and economic efficiency.
基金supported by the United States Department of Agriculture,Forest Service,Rocky Mountain Research Station through Research Joint Venture Agreement 17–098Funding was provided by the USDA Forest Service Northern Region。
文摘Understory plants are an integral part of forests,serving a variety of functions that help maintain healthy ecosystems.The structure and composition of the understory are influenced by numerous biotic and abiotic factors,with light being critical.The introduction of the pathogen Cronartium ribicola,which causes white pine blister rust,into North America in the early 20 th century led to the near total loss of western white pine(Pinus monticola)from moist forests of the Northern Rockies.Management is reintroducing blister rust-resistant western white pine across the landscape,but the effects on the understory are unknown.We examined the effects of stand structure and proportion of western white pine in the overstory on understory diversity of vascular plants in closed canopy stands dominated by blister rust-resistant western white pine across northern Idaho.Habitat series explained the greatest amount of variation(34%)in species presence-absence,while canopy cover accounted for 25%,basal area of all trees for 18%,and the proportion of western white pine composition by 14%.Our analysis revealed positive relationships between the proportion of western white pine in the overstory and both the presence of understory plants and the cover of several understory species.For both the presence and cover,separate sets of thirteen species were found to have a positive relationship with the proportion of western white pine in the overstory,with eight species in common.This research fills a knowledge gap by using data from a range of stands across northern Idaho with varying abundance of western white pine in the overstory to evaluate the relationship between the understory and overstory composition.As land managers plant more western white pine trees,we are likely to see the concomitant increase in understory plant diversity across the landscape,in addition to numerous other benefits,including disturbance resistance and resilience.
基金supported by the National Key Research and Development Program of China(No.2022YFB4300902).
文摘As joint operations have become a key trend in modern military development,unmanned aerial vehicles(UAVs)play an increasingly important role in enhancing the intelligence and responsiveness of combat systems.However,the heterogeneity of aircraft,partial observability,and dynamic uncertainty in operational airspace pose significant challenges to autonomous collision avoidance using traditional methods.To address these issues,this paper proposes an adaptive collision avoidance approach for UAVs based on deep reinforcement learning.First,a unified uncertainty model incorporating dynamic wind fields is constructed to capture the complexity of joint operational environments.Then,to effectively handle the heterogeneity between manned and unmanned aircraft and the limitations of dynamic observations,a sector-based partial observation mechanism is designed.A Dynamic Threat Prioritization Assessment algorithm is also proposed to evaluate potential collision threats from multiple dimensions,including time to closest approach,minimum separation distance,and aircraft type.Furthermore,a Hierarchical Prioritized Experience Replay(HPER)mechanism is introduced,which classifies experience samples into high,medium,and low priority levels to preferentially sample critical experiences,thereby improving learning efficiency and accelerating policy convergence.Simulation results show that the proposed HPER-D3QN algorithm outperforms existing methods in terms of learning speed,environmental adaptability,and robustness,significantly enhancing collision avoidance performance and convergence rate.Finally,transfer experiments on a high-fidelity battlefield airspace simulation platform validate the proposed method's deployment potential and practical applicability in complex,real-world joint operational scenarios.
基金Supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),the Ministry of Health&Welfare,Republic of Korea(No.RS-2020-KH088726)the Patient-Centered Clinical Research Coordinating Center(PACEN),the Ministry of Health and Welfare,Republic of Korea(No.HC19C0276)the National Research Foundation of Korea(NRF),the Korea Government(MSIT)(No.RS-2023-00247504).
文摘AIM:To evaluate long-term visual field(VF)prediction using K-means clustering in patients with primary open angle glaucoma(POAG).METHODS:Patients who underwent 24-2 VF tests≥10 were included in this study.Using 52 total deviation values(TDVs)from the first 10 VF tests of the training dataset,VF points were clustered into several regions using the hierarchical ordered partitioning and collapsing hybrid(HOPACH)and K-means clustering.Based on the clustering results,a linear regression analysis was applied to each clustered region of the testing dataset to predict the TDVs of the 10th VF test.Three to nine VF tests were used to predict the 10th VF test,and the prediction errors(root mean square error,RMSE)of each clustering method and pointwise linear regression(PLR)were compared.RESULTS:The training group consisted of 228 patients(mean age,54.20±14.38y;123 males and 105 females),and the testing group included 81 patients(mean age,54.88±15.22y;43 males and 38 females).All subjects were diagnosed with POAG.Fifty-two VF points were clustered into 11 and nine regions using HOPACH and K-means clustering,respectively.K-means clustering had a lower prediction error than PLR when n=1:3 and 1:4(both P≤0.003).The prediction errors of K-means clustering were lower than those of HOPACH in all sections(n=1:4 to 1:9;all P≤0.011),except for n=1:3(P=0.680).PLR outperformed K-means clustering only when n=1:8 and 1:9(both P≤0.020).CONCLUSION:K-means clustering can predict longterm VF test results more accurately in patients with POAG with limited VF data.
基金supported in part by the National Natural Science Foundation of China under Grant 51877061.
文摘For the ultra HVDC(UHVDC)with the hierarchical connection mode at the inverter side,considering the change of the Thevenin equivalent parameters(TEP)of post-fault AC grid,a coordinated control strategy to the subsequent commutation failure(SCF)at both layers is newly proposed.The originality of this work is manifested in three aspects.1)The mechanism of the SCF at the fault layer is newly found by deriving the analytical expression of the extinction angle with the TEP,and that at the non-fault layer is newly found by the voltage-time area theory with the DC current coupling.2)An estimation model for the TEPs of two AC grids at the inverter side is proposed with the post-fault quantities.To address the random noise and inaccurate measurement data,an adaptive robust least squares method based on the median principle is proposed to solve the TEP model.3)A coordinated control strategy with the estimated TEP is proposed to compensate for the extinction angle at the fault layer and limit the DC current at the non-fault layer,thus suppressing the SCF.The simulation results verify the suppression effect of the proposed control on the SCF under different fault conditions.
基金supported by the National Natural Science Foundation of China(Grant Nos.12472194,12002018,11972004,11772031,11402015).
文摘In this work,the Hierarchical Quadrature Element Method(HQEM)formulation of geometrically exact shells is proposed and applied for geometrically nonlinear analyses of both isotropic and laminated shells.The stress resultant formulation is developed within the HQEM framework,consequently significantly simplifying the computations of residual force and stiffness matrix.The present formulation inherently avoids shear and membrane locking,benefiting from its high-order approximation property.Furthermore,HQEM’s independent nodal distribution capability conveniently supports local p-refinement and flexibly facilitates mesh generation in various structural configurations through the combination of quadrilateral and triangular elements.Remarkably,in lateral buckling analysis,the HQEM outperforms the weak-form quadrilateral element(QEM)in accuracy with identical nodal degrees of freedom(three displacements and two rotations).Under high-load nonlinear response,the QEM exhibits a maximum relative deviation of approximately 9.5%from the reference,while the HQEM remains closely aligned with the benchmark results.In addition,for the cantilever beam under tip moment,HQEM produces virtually no out-of-plane deviation,compared to a slight deviation of 0.00001 with QEM,confirming its superior numerical reliability.In summary,the method demonstrates high accuracy,superior convergence,and robustness in handling large rotations and complex post-buckling behaviors across a series of benchmark problems.
文摘By 2025,research on Traditional Chinese Medicine(TCM)meridians has generated 12-15 macro-level theories and over 20 specific hypotheses,manifesting a highly fragmented research landscape.Objective:This paper proposes the“Holistic Hierarchical Predictive-Integration Hypothesis”(HHPIT)to construct a unified theoretical framework that integrates the rational components of existing meridian hypotheses.Methods:The HHPIT hypothesis systematically reviews current meridian theories,employs interdisciplinary methodologies,integrates artificial intelligence technology,and establishes a three-tier architecture encompassing structural,functional,and systemic layers.Results:HHPIT successfully integrates diverse meridian theories,proposes a computable algorithmic pipeline,and provides specific application protocols for chronic disease treatment,anti-aging,and enhancement of Zang-fu organ functions.Conclusion:HHPIT offers a novel,computable,and verifiable research paradigm for meridian studies,promoting the modernization and internationalization of TCM theory.
